In many contexts, computers provide suggestions to users. This is helpful because the vast number of choices that a user may have may be virtually incomprehensible. For example, in an online shopping context, a user may purchase an item but be unaware that a second item is generally used in conjunction with the purchased item. However, a computer system receiving the purchase information of the first item can identify the second item as the most frequently-purchased item, by other users, when the first item is purchased. This information is helpful to the user in that he or she may have been totally unaware of the existence and/or necessity of the second item.
Online shopping systems, such as Amazon, and/or online content systems, such as Netflix, are able to analyze the activities of millions of users in order to improve or optimize purchasing events for a comparatively small set of products (i.e. movies). However, the approaches used for generating recommendations to users in such high traffic/small product set scenarios may not work effectively when the amount of user traffic is not plentiful and/or when the set of potential items to recommend is very large. Further, prior approaches may not work as effectively when the amount of traffic and/or the amount of items vary significantly.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.